Course Description
An introduction to data analysis for health and medical applications, covering various regression models for different outcome types, including continuous, dichotomous, and count outcomes. Additional topics include regression models for correlated data, mixed-effect models, and Bayesian inference. The course emphasizes the use of R software.
Learning Objectives
By the end of this course, students should be able to:
- Perform preliminary analysis for health data using regression models.
- Specify appropriate models based on outcome type.
- Conduct model diagnostics and assess goodness of fit.
- Identify directions for further statistical analysis if needed.
- Understand and apply Bayesian inference in health data analysis.
Course Description
This course bridges traditional biostatistical concepts with modern machine learning methodologies. It aims to develop a deeper understanding of machine learning insights while strengthening statistical foundations. The course covers supervised and unsupervised learning techniques, their applications in epidemiology and biostatistics, and associated challenges.
Learning Objectives
By the end of this course, students should be able to:
- Differentiate between supervised and unsupervised learning techniques.
- Understand and apply statistical decision theory.
- Analyze classification models including LDA, QDA, and support vector machines.
- Implement tree-based models, neural networks, and deep learning techniques.
- Evaluate the interpretability and ethical considerations of machine learning in biostatistics.
Course Description
An examination of statistical issues aiming towards statistical literacy and appropriate interpretation of statistical information. Common misconceptions will be targeted. Assessment of the validity and treatment of results in popular and scientific media. Conceptual consideration of study design, numerical and graphical data summaries, probability, sampling variability, confidence intervals, and hypothesis tests.
Learning Objectives
By the end of this course, students should be able to:
- Correctly use and understand foundational vocabulary concepts associated with statistics.
- Interpret, create (with the aid of suitable applets/technology), and critically evaluate graphical and numerical summaries of data.
- Understand the role of chance, randomness, and 'average' in the context of statistical research design and analysis.
- Evaluate and critique the validity of statistical research designs and conclusions.
- Evaluate statistical information presented in media and society.
Course Materials
